A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering (2020)

Mahini, R., Li, Y., Ding, W., Fu, R., Ristaniemi, T., Nandi, A. K., Chen, G., & Cong, F. (2020). Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering. Frontiers in Neuroscience, 14, Article 521595. https://doi.org/10.3389/fnins.2020.521595

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Mahini, Reza; Li, Yansong; Ding, Weiyan; Fu, Rao; Ristaniemi, Tapani; Nandi, Asoke K.; Chen, Guoliang; Cong, Fengyu

Lehti tai sarja: Frontiers in Neuroscience

ISSN: 1662-4548

eISSN: 1662-453X

Julkaisuvuosi: 2020

Ilmestymispäivä: 21.10.2020

Volyymi: 14

Artikkelinumero: 521595

Kustantaja: Frontiers Media SA

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

DOI: https://doi.org/10.3389/fnins.2020.521595

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/72335

Tiivistelmä

Clustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects’ data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed by a newly proposed time-window detection method to determine the most suitable time window for identifying the ERP of interest in each condition/group. Applying the proposed method to the simulated ERP data and real data indicated that the brain responses from the individual subjects can be collected to determine a reliable time window for different conditions/groups. Our results revealed more precise time windows to identify N2 and P3 components in the simulated data compared to the state-of-the-art methods. Additionally, our proposed method achieved more robust performance and outperformed statistical analysis results in the real data for N300 and prospective positivity components. To conclude, the proposed method successfully estimates the time window for ERP of interest by processing the individual data, offering new venues for spatiotemporal ERP processing.

YSO-asiasanat: kognitiivinen neurotiede; signaalianalyysi; signaalinkäsittely; klusterianalyysi

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1